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Cross Entropy

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data visualization

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determinant

diagonal-matrix

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doing-literature-review

Domain vs. Codomain vs. Range

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Dynamic Programming (DP) in python

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eigenvalue-eigenvector

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How to prepare for Behavioral Interview

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identity-matrix

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Instructional Websites

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joint-distribuition

jupyter-notebook-on-server

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logarithm

Logistic Regression vs. Neural Network

Logistic Regression

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lp-norm

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Machine Learning Algorithm Selection

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Manhattan Distance

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Masked Language Modeling

matplotlib functions

matplotlib legend

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max-norm

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Mean Squared Error (MSE)

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Mean

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Merge Overlapping Intervals

Meteor Score

Min Max Normalization

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ML Case Study or ML Design

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MRR

Multi Class Cross Entropy

Multi Label Cross Entropy

Multi Layer Perceptron

Multicollinearity

Multiset

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Multivariate Normal Distribution

Mutual Information

N-gram Method

Naive Bayes

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Next Sentence Prediction

norm

Normal Distribution

Normalization

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Odds

One Class Classification

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One vs One Multi Class Classification

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Optimizers

orthogonal-matrix

orthonormal-vector

Overcomplete Autoencoder

Overfitting

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p-value

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Plots Compared

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Precision Recall Curve (PRC)

Precision

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Prior Probability

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Probability vs. Likelihood

Problem Solving Algorithm Selection

Proximal Policy Optimization (PPO)

Pruning in Decision Tree

PyTorch Loss Functions

PyTorch Refresher

Questions to ask in a Interview?

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Quotient Rule or Differentiation of Division

R-squared Value

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Random Forest

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Recall

Recall@K

Recommender System (RecSys)

Regularization

Reinforcement Learning from Human Feedback (RLHF)

Reinforcement Learning

Relational GCN

ReLU

Reno Talk @UMBC on Scale-2024

Retrieval Metrics

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ROC Curve

Root Mean Squared Error (RMSE)

Root Mean Squared Logarithmic Error (RMSLE)

ROUGE-L Score

ROUGE-LSUM Score

ROUGE-N Score

Saddle Points

scalar

Second Order Derivative or Hessian Matrix

Self Attention vs. Cross Attention

Self-Supervised Learning

Semi-supervised Learning

Sensitivity

SentencePiece Tokenization

Sequence-to-Sequence Model

Sigmoid Function

Sigmoid Kernel

Simple Linear Regression

Singular Value Decomposition (SVD)

Skip Gram Model

SMOTE

Soft Margin in SVM

Softmax

Softplus

Softsign

Some Common Behavioral Questions

Sources of Uncertainty

spacy-doc-object

spacy-doc-span-token

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spacy-pipeline

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Specificity

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Standard deviation

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Standardization

Statistical Power

Statistical Significance

Stemming

Stochastic Gradient Descent (SGD)

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Stop Words

Stratified K Fold Cross Validation

Stride in CNN

Stump

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Sub-word Tokenizer

Supervised Learning

Support Vector Machine (SVM)

Support Vector

Surprise

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Swallow vs. Deep Learning

t-SNE

Tanh

Text Preprocessing

TF-IDF

Three Way Partioning

Time Complexity of ML Algos

Time Complexity of ML Models

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Top-K in Retrieval System

trace-operator

Training a Deep Neural Network

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Triplet Loss

True Negative Rate

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Type 1 Error vs. Type 2 Error

Undercomplete Autoencoder

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unit-vector

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ML Interview

#interview #machine-learning #deep-learning #nlp #vision

#math
#statistics
- Histogram
- Distribution ⭐️
  - Uniform Distribution
  - Normal Distribution
  - Multivariate Normal Distribution
  - Multinomial Normal Distribution
  - Gaussian Distribution
  - Exponential Distribution
  - Binomial Distribution
  - Poisson Distribution
  - Population
- Mean
- Mode
- Median
- Variance
- Standard deviation
- Co-Variance
- Finding Co-relation between two data or distribution
  - Pearson Correlation
  - R-squared Value
  - Mutual Information
  - Cosine Similarity ⭐️
  - Co-Variance
  - Jaccard index
  - Chi Squared Test
- Distance Metric
  - Manhattan Distance
  - Euclidian Distance
  - Cosine Similarity
  - Mahalanobis Distance
  - Hamming Distance
  - Chebychev Distance
- Hypothesis Testing
- Null Hypothesis
- Statistical Test
- p-value
- Odds
  - Log (Odds)
- Central Limit Theorem
- Quintile or Percentile
- Log Scale
#probability
#visualization
- Plots Compared
#machine-learning
- Supervised Learning
  - Linear Regression ⭐️
  - Polynomial Regression
  - Bayesian Regression
  - Logistic Regression ⭐️
    - Multinomial Logistic Regression
  - Perceptron ⭐️
  - Multi Layer Perceptron ⭐️
  - GLM
  - LDA
  - UMAP
  - t-SNE
  - Support Vector Machine (SVM) ⭐️
    - SVR ⭐️
    - SVC
    - Kernel in SVM
  - K-nearest Neighbor (KNN)
  - Decision Tree ⭐️
    - GBM
    - Adaboost
    - XGBoost
    - LightGBM
    - CatBoost
    - Pruning in Decision Tree
  - Ensemble Learning
    - Bagging
      - Random Forest ⭐️
    - Boosting
      - Gradient Boosting ⭐️
    - How to combine in Ensemble Learning
  - Naive Bayes
    - Gaussian ⭐️
    - Multinomial ⭐️
    - Bernouli
    - Complement
    - Categorical
  - Markov Chain
- Unsupervised Learning
  - Clustering
    - K-means Clustering ⭐️
    - Hierarchical Clustering
    - DBScan Clustering
    - HDBScan Clustering
    - K-means vs. Hierarchical
    - Spectral Clustering
    - Gaussian Mixture Model
  - Dimensionality Reduction
    - Principal Component Analysis (PCA) ⭐️
    - UMAP
    - HeatMap
    - t-SNE plots
    - Autoencoder
  - Association
    - Apriori
  - Expectation Minimization
- Semi-supervised Learning
- Recommendation
  - Content Filtering ⭐️
  - Collaborative Filtering ⭐️
- Metric Learning
- Learning to Rank
  - Pointwise Learning to Rank
  - Pairwise Learning to Rank
  - Listwise Learning to Rank
- Probabilistic Graphical Model
  - Conditional Random Field
  - Bayessian Network
#deep-learning
- CNN
- RNN ⭐️
- LSTM ⭐️
- Bidirectional RNN or LSTM ⭐️
- GRU ⭐️
- Autoencoder
  - Standard ⭐️
  - Variational Autoencoder ⭐️
  - PCA vs. Autoencoder
  - Overcomplete Autoencoder
  - Undercomplete Autoencoder
  - Uses: ⭐️
    - Autoencoder for Anomaly Detection
    - Autoencoder for Denoising Images
    - Representation Learning
- Attention Reference
  - Self Attention ⭐️
  - Masked Self Attention ⭐️
  - Multihead Self Attention ⭐️
  - Encoder-Decoder Attention
  - Factorized Self Attention
  - Flash Attention
  - Cross Attention
- Transformer
  - Encoder-decoder ⭐️
  - Encoder Only ⭐️
  - Decoder Only ⭐️
- Contrastive Learning ⭐️
- Graph Convolutional Network (GCN) ⭐️
  - Relational GCN
  - Graph Attention Network
- Word Embeddings
  - TF-IDF ⭐️
  - Word2Vec ⭐️ Ref
    - Continuous Bag of Words (CBOW)
    - Skip Gram Model
  - FastText ⭐️
  - Glove ⭐️
  - Elmo
  - BERT Embeddings
- Activation Function
  - Sigmoid Function ⭐️
  - Tanh ⭐️
  - Softplus
  - Softsign
  - Softmax ⭐️
  - ReLU ⭐️
  - Leaky ReLU
  - PReLU
  - ELU
  - SELU
  - Swiss ReLU
  - GeLU
- Optimizers
  - Gradient Descent ⭐️
  - Stochastic Gradient Descent (SGD) ⭐️
  - Mini Batch SGD ⭐️
  - Stochastic Gradient Descent with Momentum
  - Nesterov Accelerated Gradient (NAG)
  - Adaptive Methods
    - AdaGrad
    - AdaDelta
    - RMSProp
    - Adam
    - Adamax
    - AMSGrad
    - NADAM
- Generative Adversarial Network
- Genetic Algorithms
- Reinforcement Learning
#loss-in-ml
- Entropy ⭐️
- Cross Entropy
  - Multi Class Cross Entropy ⭐️
  - Multi Label Cross Entropy ⭐️
- KL Divergence ⭐️
- Contrastive Loss ⭐️
- Triplet Loss ⭐️
- InfoNCE Loss ⭐️
- Mean Squared Error (MSE) ⭐️
- Mean Absolute Error (MAE)
- Mean Squared Logarithmic Error (MSLE)
- Mean Absolute Percentage Error (MAPE)
- Huber Loss
- Log-cosh Loss
- Poisson Loss
- Hinge Loss
#evaluation
- Extrinsic Evaluation
- Intrinsic Evaluation
- Perplexity ⭐️
- Precision
- Recall
- Accuracy
- F1 Score ⭐️
- Sensitivity ⭐️
- Specificity ⭐️
- True Positive Rate
- False Positive Rate
- Confusion Matrix ⭐️
- Bias & Variance ⭐️
- AUC Score
- ROC Curve
- BLEU Score ⭐️
- ROUGE-N Score ⭐️
- ROUGE-L Score ⭐️
- Meteor Score
- BERTScore
- Mean Squared Error (MSE)
- Mean Absolute Error (MAE)
- Root Mean Squared Error (RMSE)
- Mean Absolute Percentage Error (MAPE)
- R-squared Value
- Root Mean Squared Logarithmic Error (RMSLE)
Regularization
- L1 or Lasso Regression ⭐️
- L2 or Ridge Regression ⭐️
- Elastic Net Regression
- Dropout ⭐️
Misc.
- Machine Learning vs. Deep Learning
- Cross Validation
- Multi Class Classification
  - One vs Rest or One vs All Multi Class Classification
  - One vs One Multi Class Classification
- Internal Covariate Shift
- Discriminative vs. Generative Models
- Kernel Regression
- One Class Classification
  - One Class Gaussian
  - One Class K-means
  - One Class KNN
  - One Class SVM
- Gumble Softmax ⭐️
- Normalization
  - Normalization
  - Batch Normalization
  - Layer Normalization
- Generation
  - Greedy Decoding ⭐️
  - Beam Search ⭐️
  - Random Sampling ⭐️
  - Minimum Bayes Risk
- Handling Missing Data ⭐️
- Overfitting ⭐️
- Handling Imbalanced Dataset ⭐️
  - SMOTE
  - ADASYN
- Handling Outliers ⭐️
- Tokenizer
  - Byte Pair Encoding (BPE) ⭐️
  - WordPiece Tokenization
  - SentencePiece Tokenization
- Parametric vs Non Parametric ⭐️
- Model Based vs. Instance Based Learning ⭐️
- Swallow vs. Deep Learning ⭐️
- Parameter vs. Hyperparameter ⭐️
- Exploding Gradient ⭐️
- Vanishing Gradient ⭐️
- Hyperparameters
- Loss vs. Cost
- Gradient Clipping
- Grad accumulation
- Stemming
- Lemmatization
- Causality vs. Correlation
- Negative Sampling
- Data Augmentation
- Data Imputation
- Hinge Loss
- Feature Selection
- Framenet
- Wordnet
- Verbnet
- AMR Graph
- Transfer Learning
- Teacher Forcing ⭐️
- Student Forcing ⭐️
- Curriculum Learning ⭐️
- Weight Initialization
  - Xavier
  - Normal
- Learning Rate Scheduler ⭐️
- Fine Tuning Speedup
  - LORA ⭐️
  - Adapter ⭐️
- Hyper parameter finding
  - Grid Search Hyperparameter Finding
  - Random Search
  - Bayesian Optimization Hyperparameter Finding
  - Genetic Algorithm Hyperparameter Finding
  - Gradient based techniques
- Different types of Learning
  - Zero Shot Learning
  - One Shot Learning
  - Few Shot Learning
  - Transfer Learning
  - Active Learning
- Idea about SOTA Research
  - LLaMA
  - ChatGPT
  - BERT Ref
  - BART
  - GPT GPT-2
  - ROBERTA
  - ALbert
  - XLNET
  - Electra
  - DistilBert
  - [ ]
- ELBO
- End to End Machine Learning Pipeline
- Convex vs. Non-Convex
- Convex vs. Non-Convex Optimization
- One Hot Vector
- LabelEncoding
- One Hot Encoding vs. Label Encoding
- Inductive Bias
- Selection Bias
- Type 1 Error vs. Type 2 Error

Related Notes

CNN
Padding in CNN
Stride in CNN
Autoencoder for Denoising Images

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